Machine Learning-Based Highway Truck Commodity Classification Using Logo Data
Source abstract
Abstract
In this paper, we propose a novel approach to commodity classification from surveillance videos by utilizing logo data on trucks. Broadly, most logos can be classified as predominantly text or predominantly images. For the former, we leverage state-of-the-art deep-learning-based text recognition algorithms on images. For the latter, we develop a two-stage image retrieval algorithm consisting of a universal logo detection stage that outputs all potential logo positions, followed by a logo recognition stage designed to incorporate advanced image representations. We develop an integrated approach to combine predictions from both the text-based and image-based solutions, which can help determine the commodity type that is potentially being hauled by trucks. We evaluated these models on videos collected in collaboration with the state transportation entity and achieved promising performance. This, along with prior work on trailer classification, can be effectively used for automatically deriving commodity types for trucks moving on highways.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Vehicle License Plate Recognition · Handwritten Text Recognition Techniques · Advanced Neural Network Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL 32611, USA
References (44)
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